# import torch
# import torchvision
# from torch.nn import Linear
# from torch.utils.data import DataLoader
# from torch import nn
#
# dataset=torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
# dataloader =DataLoader(dataset,batch_size=64)
#
# class JJw(nn.Module):
#     def __init__(self):
#         super(JJw,self).__init__()
#         self.linear1=Linear(196608,10)
#
#     def forward(self,input):
#         output=self.linear1(input)
#         return output
#
# jjw=JJw()
#
# for data in dataloader:
#     imgs,targets=data
#     # output=torch.reshape(imgs,(1,1,1,-1))
#     output=torch.flatten(imgs)
#     output=jjw(output)
#     print(output.shape)
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter


class JJw(nn.Module):
    def __init__(self):
        super(JJw,self).__init__()
        # self.conv1=Conv2d(3,32,5,padding=2,stride=1)
        # self.maxpool1=MaxPool2d(2)
        # self.conv2=Conv2d(32,32,5,padding=2)
        # self.maxpool2=MaxPool2d(2)
        # self.conv3=Conv2d(32,64,5,padding=2)
        # self.maxpool3=MaxPool2d(2)
        # self.flatten=Flatten()
        # self.linear1=Linear(1024,64)
        # self.linear2=Linear(64,10)

        self.model1=Sequential(
            Conv2d(3, 32, 5, padding=2, stride=1),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self,x):
        # x=self.conv1(x)
        # x=self.maxpool1(x)
        # x = self.conv2(x)
        # x =self.maxpool2(x)
        # x=self.conv3(x)
        # x=self.maxpool3(x)
        # x=self.flatten(x)
        # x=self.linear1(x)
        # x=self.linear2(x)
        x=self.model1(x)
        return x

jjw=JJw()
print(jjw)

input=torch.ones(64,3,32,32)
output=jjw(input)
print(output.shape)

writer=SummaryWriter("./logs")
writer.add_graph(jjw,input)
writer.close()


